The signal peptide of yeast killer toxin K2 confers producer self-protection and allows conversion into a modular toxin-immunity system

Some microbial toxins also target the producer species itself, necessitating a means of self-protection. The M2 double-stranded RNA (dsRNA) killer virus in Saccharomyces cerevisiae contains a single open reading frame (ORF) encoding both the secreted pore-forming toxin K2 as well as a cognate immunity factor. Here, we show that expression of a 49-amino acid N-terminal peptide from the K2 precursor is both necessary and sufficient for immunity. This immunity peptide simultaneously functions as a signal peptide for toxin secretion and protects the cell against the cytotoxic K2 α subunit. The K2 toxin and immunity factor can be functionally separated into two ORFs, yielding a modular toxin-immunity system. This case further shows how a (signal) peptide can carry the potential for providing cellular protection against an antimicrobial toxin.

City-scale monitoring of antibiotic resistance genes by digital PCR and metagenomics

BACKGROUND

Anthropogenic activities significantly contribute to the dissemination of antibiotic resistance genes (ARGs), posing a substantial threat to humankind. The development of methods that allow robust ARG surveillance is a long-standing challenge. Here, we use city-scale monitoring of ARGs by using two of the most promising cutting-edge technologies, digital PCR (dPCR) and metagenomics.

METHODS

ARG hot-spots were sampled from the urban water and wastewater distribution systems. Metagenomics was used to provide a broad view of ARG relative abundance and richness in the prokaryotic and viral fractions. From the city-core ARGs in all samples, the worldwide dispersed sul2 and tetW conferring resistance to sulfonamide and tetracycline, respectively, were monitored by dPCR and metagenomics.

RESULTS

The largest relative overall ARG abundance and richness were detected in the hospital wastewater and the WWTP inlet (up to ≈6,000 ARGs/Gb metagenome) with a large fraction of unclassified resistant bacteria. The abundance of ARGs in DNA and RNA contigs classified as viruses was notably lower, demonstrating a reduction of up to three orders of magnitude compared to contigs associated to prokaryotes. By metagenomics and dPCR, a similar abundance tendency of sul2 and tetW was obtained, with higher abundances in hospital wastewater and WWTP input (≈125-225 ARGs/Gb metagenome). dPCR absolute abundances were between 6,000 and 18,600 copies per ng of sewage DNA (≈105-7 copies/mL) and 6.8 copies/mL in seawater near the WWTP discharging point.

CONCLUSIONS

dPCR was more sensitive and accurate, while metagenomics provided broader coverage of ARG detection. While desirable, a reliable correlation of dPCR absolute abundance units into metagenomic relative abundance units was not obtained here (r2 < 0.4) suggesting methodological factors that introduce variability. Evolutionary pressure does not significantly select the targeted ARGs in natural aquatic environments.

Chapter 2: What Is Life?

The question "What is life?" has existed since the beginning of recorded history. However, the scientific and philosophical contexts of this question have changed and been refined as advancements in technology have revealed both fine details and broad connections in the network of life on Earth. Understanding the framework of the question "What is life?" is central to formulating other questions such as "Where else could life be?" and "How do we search for life elsewhere?" While many of these questions are addressed throughout the Astrobiology Primer 3.0, this chapter gives historical context for defining life, highlights conceptual characteristics shared by all life on Earth as well as key features used to describe it, discusses why it matters for astrobiology, and explores both challenges and opportunities for finding an informative operational definition.

RNAVirHost: a machine learning-based method for predicting hosts of RNA viruses through viral genomes

BACKGROUND

The high-throughput sequencing technologies have revolutionized the identification of novel RNA viruses. Given that viruses are infectious agents, identifying hosts of these new viruses carries significant implications for public health and provides valuable insights into the dynamics of the microbiome. However, determining the hosts of these newly discovered viruses is not always straightforward, especially in the case of viruses detected in environmental samples. Even for host-associated samples, it is not always correct to assign the sample origin as the host of the identified viruses. The process of assigning hosts to RNA viruses remains challenging due to their high mutation rates and vast diversity.

RESULTS

In this study, we introduce RNAVirHost, a machine learning-based tool that predicts the hosts of RNA viruses solely based on viral genomes. RNAVirHost is a hierarchical classification framework that predicts hosts at different taxonomic levels. We demonstrate the superior accuracy of RNAVirHost in predicting hosts of RNA viruses through comprehensive comparisons with various state-of-the-art techniques. When applying to viruses from novel genera, RNAVirHost achieved the highest accuracy of 84.3%, outperforming the alignment-based strategy by 12.1%.

CONCLUSIONS

The application of machine learning models has proven beneficial in predicting hosts of RNA viruses. By integrating genomic traits and sequence homologies, RNAVirHost provides a cost-effective and efficient strategy for host prediction. We believe that RNAVirHost can greatly assist in RNA virus analyses and contribute to pandemic surveillance.

Structural and computational studies of HIV-1 RNA

Viruses remain a global threat to animals, plants, and humans. The type 1 human immunodeficiency virus (HIV-1) is a member of the retrovirus family and carries an RNA genome, which is reverse transcribed into viral DNA and further integrated into the host-cell DNA for viral replication and proliferation. The RNA structures from the HIV-1 genome provide valuable insights into the mechanisms underlying the viral replication cycle. Moreover, these structures serve as models for designing novel therapeutic approaches. Here, we review structural data on RNA from the HIV-1 genome as well as computational studies based on these structural data. The review is organized according to the type of structured RNA element which contributes to different steps in the viral replication cycle. This is followed by an overview of the HIV-1 transactivation response element (TAR) RNA as a model system for understanding dynamics and interactions in the viral RNA systems. The review concludes with a description of computational studies, highlighting the impact of biomolecular simulations in elucidating the mechanistic details of various steps in the HIV-1's replication cycle.

Coronavirus takeover of host cell translation and intracellular antiviral response: a molecular perspective

Coronaviruses are a group of related RNA viruses that cause respiratory diseases in humans and animals. Understanding the mechanisms of translation regulation during coronaviral infections is critical for developing antiviral therapies and preventing viral spread. Translation of the viral single-stranded RNA genome in the host cell cytoplasm is an essential step in the life cycle of coronaviruses, which affects the cellular mRNA translation landscape in many ways. Here we discuss various viral strategies of translation control, including how members of the Betacoronavirus genus shut down host cell translation and suppress host innate immune functions, as well as the role of the viral non-structural protein 1 (Nsp1) in the process. We also outline the fate of viral RNA, considering stress response mechanisms triggered in infected cells, and describe how unique viral RNA features contribute to programmed ribosomal -1 frameshifting, RNA editing, and translation shutdown evasion.

HostNet: improved sequence representation in deep neural networks for virus-host prediction

BACKGROUND

The escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a challenging and pressing demand.

RESULTS

To overcome the challenges of virus-host prediction, we present HostNet, a deep learning framework that utilizes a Transformer-CNN-BiGRU architecture and two enhanced sequence representation modules. The first module, k-mer to vector, pre-trains a background vector representation of k-mers from a broad range of virus sequences to address the issue of data deficiency. The second module, an adaptive sliding window, truncates virus sequences of various lengths to create a uniform number of informative and distinct samples for each sequence to address the issue of data imbalance. We assess HostNet's performance on a benchmark dataset of "Rabies lyssavirus" and an in-house dataset of "Flavivirus". Our results show that HostNet surpasses the state-of-the-art deep learning-based method in host-prediction accuracies and F1 score. The enhanced sequence representation modules, significantly improve HostNet's training generalization, performance in challenging classes, and stability.

CONCLUSION

HostNet is a promising framework for predicting virus hosts from genomic sequences, addressing challenges posed by sparse and varying-length virus sequence data. Our results demonstrate its potential as a valuable tool for virus-host prediction in various biological contexts. Virus-host prediction based on genomic sequences using deep neural networks is a promising approach to identifying their potential hosts accurately and efficiently, with significant impacts on public health, disease prevention, and vaccine development.

Infectious viruses from transfected SARS-CoV-2 genomic RNA

SARS-CoV-2 emerged at the end of 2019, and like other novel pathogens causing severe symptoms, WHO recommended heightened biosafety measures for laboratories working with the virus. The positive-stranded genomic RNA of coronaviruses has been known to be infectious since the 1970s, and overall, all experiments with the possibility of SARS-CoV-2 propagation are carried out in higher containment level laboratories. However, as SARS-CoV-2 RNA has been routinely handled in BSL-2 laboratories, the question of the true nature of RNA infectiousness has risen along with discussion of appropriate biosafety measures. Here, we studied the ability of native SARS-CoV-2 genomic RNA to produce infectious viruses when transfected into permissive cells and discussed the biosafety control measures related to these assays. In transfection assays large quantities of genomic vRNA of SARS-CoV-2 was required for a successful production of infectious viruses. However, the quantity of vRNA alone was not the only factor, and especially when the transfected RNA was derived from infected cells, even small amounts of genomic vRNA was enough for an infection. Virus replication was found to start rapidly after transfection, and infectious viruses were detected in the cell culture media at 24 h post-transfection. In addition, silica membrane-based kits were shown to be as good as traditional TRI-reagent based methods in extracting high-quality, 30 kb-long genomic vRNA. Taken together, our data indicates that all transfection experiments with samples containing genomic SARS-CoV-2 RNA should be categorized as a propagative work and the work should be conducted only in a higher containment BSL-3 laboratory.

Single-cell RNA-seq methods to interrogate virus-host interactions

The twenty-first century has seen the emergence of many epidemic and pandemic viruses, with the most recent being the SARS-CoV-2-driven COVID-19 pandemic. As obligate intracellular parasites, viruses rely on host cells to replicate and produce progeny, resulting in complex virus and host dynamics during an infection. Single-cell RNA sequencing (scRNA-seq), by enabling broad and simultaneous profiling of both host and virus transcripts, represents a powerful technology to unravel the delicate balance between host and virus. In this review, we summarize technological and methodological advances in scRNA-seq and their applications to antiviral immunity. We highlight key scRNA-seq applications that have enabled the understanding of viral genomic and host response heterogeneity, differential responses of infected versus bystander cells, and intercellular communication networks. We expect further development of scRNA-seq technologies and analytical methods, combined with measurements of additional multi-omic modalities and increased availability of publicly accessible scRNA-seq datasets, to enable a better understanding of viral pathogenesis and enhance the development of antiviral therapeutics strategies.

RNA Viruses Linked to Eukaryotic Hosts in Thawed Permafrost

Arctic permafrost is thawing due to global warming, with unknown consequences on the microbial inhabitants or associated viruses. DNA viruses have previously been shown to be abundant and active in thawing permafrost, but little is known about RNA viruses in these systems. To address this knowledge gap, we assessed the composition of RNA viruses in thawed permafrost samples that were incubated for 97 days at 4°C to simulate thaw conditions. A diverse RNA viral community was assembled from metatranscriptome data including double-stranded RNA viruses, dominated by Reoviridae and Hypoviridae, and negative and positive single-stranded RNA viruses, with relatively high representations of Rhabdoviridae and Leviviridae, respectively. Sequences corresponding to potential plant and human pathogens were also detected. The detected RNA viruses primarily targeted dominant eukaryotic taxa in the samples (e.g., fungi, Metazoa and Viridiplantae) and the viral community structures were significantly associated with predicted host populations. These results indicate that RNA viruses are linked to eukaryotic host dynamics. Several of the RNA viral sequences contained auxiliary metabolic genes encoding proteins involved in carbon utilization (e.g., polygalacturosase), implying their potential roles in carbon cycling in thawed permafrost. IMPORTANCE Permafrost is thawing at a rapid pace in the Arctic with largely unknown consequences on ecological processes that are fundamental to Arctic ecosystems. This is the first study to determine the composition of RNA viruses in thawed permafrost. Other recent studies have characterized DNA viruses in thawing permafrost, but the majority of DNA viruses are bacteriophages that target bacterial hosts. By contrast RNA viruses primarily target eukaryotic hosts and thus represent potential pathogenic threats to humans, animals, and plants. Here, we find that RNA viruses in permafrost are novel and distinct from those in other habitats studied to date. The COVID-19 pandemic has heightened awareness of the importance of potential environmental reservoirs of emerging RNA viral pathogens. We demonstrate that some potential pathogens were detected after an experimental thawing regime. These results are important for understanding critical viral-host interactions and provide a better understanding of the ecological roles that RNA viruses play as permafrost thaws.

The Viral Fraction Metatranscriptomes of Lake Baikal

This article characterises viral fraction metatranscriptomes (smaller than 0.2 µm) from the pelagic zone of oligotrophic Lake Baikal (Russia). The study revealed the dominance of transcripts of DNA viruses: bacteriophages and algal viruses. We identified transcripts similar to Pithovirus sibericum, a nucleocytoplasmic large DNA virus (NCLDV) isolated from the permafrost region of Eastern Siberia. Among the families detected were RNA viruses assigned to Retroviridae, Metaviridae, Potyviridae, Astroviridae, and Closteroviridae. Using the PHROG, SEED subsystems databases, and the VOGDB, we indicated that the bulk of transcripts belong to the functional replication of viruses. In a comparative unweighted pair group method with arithmetic mean (UPGMA) analysis, the transcripts from Lake Baikal formed a separate cluster included in the clade with transcripts from other freshwater lakes, as well as marine and oceanic waters, while there was no separation based on the trophic state of the water bodies, the size of the plankton fraction, or salinity.

Accelerated Evolution by Diversity-Generating Retroelements

Diversity-generating retroelements (DGRs) create vast amounts of targeted, functional diversity by facilitating the rapid evolution of ligand-binding protein domains. Thousands of DGRs have been identified in bacteria, archaea, and their respective viruses. They are broadly distributed throughout the microbial world, with enrichment observed in certain taxa and environments. The diversification machinery works through a novel mechanism termed mutagenic retrohoming, whereby nucleotide sequence information is copied from an invariant DNA template repeat (TR) into an RNA intermediate, selectively mutagenized at TR adenines during cDNA synthesis by a DGR-encoded reverse transcriptase, and transferred to a variable repeat (VR) region within a variable-protein gene (54). This unidirectional flow of information leaves TR-DNA sequences unmodified, allowing for repeated rounds of mutagenic retrohoming to optimize variable-protein function. DGR target genes are often modular and can encode one or more of a wide variety of discrete functional domains appended to a diversifiable ligand-binding motif. Bacterial variable proteins often localize to cell surfaces, although a subset appear to be cytoplasmic, while phage-encoded DGRs commonly diversify tail fiber-associated receptor-binding proteins. Here, we provide a comprehensive review of the mechanism and consequences of accelerated protein evolution by these unique and beneficial genetic elements. Expected final online publication date for the Annual Review of Microbiology, Volume 76 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Structural Analysis of Monomeric RNA-Dependent Polymerases Revisited

In the past few years, our understanding of the RNA virosphere has changed dramatically due to the growth and spurt of metagenomics, exponentially increasing the number of RNA viral sequences, and providing a better understanding of their range of potential hosts. As of today, the only conserved protein among RNA viruses appears to be the monomeric RNA-dependent RNA polymerase. This enzyme belongs to the right-hand DNA-and RNA polymerases, which also includes reverse transcriptases and eukaryotic replicative DNA polymerases. The ubiquity of this protein in RNA viruses makes it a unique evolutionary marker and an appealing broad-spectrum antiviral target. In this work pairwise structural comparisons of viral RdRps and RTs were performed, including tertiary structures that have been obtained in the last few years. The resulting phylogenetic tree shows that the RdRps from (+)ss- and dsRNA viruses might have been recruited several times throughout the evolution of mobile genetic elements. RTs also display multiple evolutionary routes. We have identified a structural core comprising the entire palm, a large moiety of the fingers and the N-terminal helices of the thumb domain, comprising over 300 conserved residues, including two regions that we have named the “knuckles” and the “hypothenar eminence”. The conservation of an helix bundle in the region preceding the polymerase domain confirms that (−)ss and dsRNA Reoviruses’ polymerases share a recent ancestor. Finally, the inclusion of DNA polymerases into our structural analyses suggests that monomeric RNA-dependent polymerases might have diverged from B-family polymerases.

Characterization of the SARS-CoV-2 coronavirus X4-like accessory protein

Background

The novel coronavirus SARS-CoV-2 is currently a global threat to health and economies. Therapeutics and vaccines are in rapid development; however, none of these therapeutics are considered as absolute cure, and the potential to mutate makes it necessary to find therapeutics that target a highly conserved regions of the viral structure.

Results

In this study, we characterized an essential but poorly understood coronavirus accessory X4 protein, a core and stable component of the SARS-CoV family. Sequence analysis shows a conserved ~ 90% identity between the SARS-CoV-2 and previously characterized X4 protein in the database. QMEAN Z score of the model protein shows a value of around 0.5, within the acceptable range 0-1. A MolProbity score of 2.96 was obtained for the model protein and indicates a good quality model. The model has Ramachandran values of φ = - 57o and ψ = - 47o for α-helices and values of φ = - 130o and ψ = + 140o for twisted sheets.

Conclusions

The protein data obtained from this study provides robust information for further in vitro and in vivo experiment, targeted at devising therapeutics against the virus. Phylogenetic analysis further supports previous evidence that the SARS-CoV-2 is positioned with the SL-CoVZC45, BtRs-BetaCoV/YN2018B and the RS4231 Bat SARS-like corona viruses.

Structural analysis of viral ExoN domains reveals polyphyletic hijacking events

Nidoviruses and arenaviruses are the only known RNA viruses encoding a 3’-5’ exonuclease domain (ExoN). The proofreading activity of the ExoN domain has played a key role in the growth of nidoviral genomes, while in arenaviruses this domain partakes in the suppression of the host innate immune signaling. Sequence and structural homology analyses suggest that these proteins have been hijacked from cellular hosts many times. Analysis of the available nidoviral ExoN sequences reveals a high conservation level comparable to that of the viral RNA-dependent RNA polymerases (RdRp), which are the most conserved viral proteins. Two highly preserved zinc fingers are present in all nidoviral exonucleases, while in the arenaviral protein only one zinc finger can be identified. This is in sharp contrast with the reported lack of zinc fingers in cellular ExoNs, and opens the possibility of therapeutic strategies in the struggle against COVID-19.

On the Verge of Life: Distribution of Nucleotide Sequences in Viral RNAs

The aim of the study is to analyze viruses using parameters obtained from distributions of nucleotide sequences in the viral RNA. Seeking for the input data homogeneity, we analyze single-stranded RNA viruses only. Two approaches are used to obtain the nucleotide sequences; In the first one, chunks of equal length (four nucleotides) are considered. In the second approach, the whole RNA genome is divided into parts by adenine or the most frequent nucleotide as a "space". Rank-frequency distributions are studied in both cases. The defined nucleotide sequences are signs comparable to a certain extent to syllables or words as seen from the nature of their rank-frequency distributions. Within the first approach, the Pólya and the negative hypergeometric distribution yield the best fit. For the distributions obtained within the second approach, we have calculated a set of parameters, including entropy, mean sequence length, and its dispersion. The calculated parameters became the basis for the classification of viruses. We observed that proximity of viruses on planes spanned on various pairs of parameters corresponds to related species. In certain cases, such a proximity is observed for unrelated species as well calling thus for the expansion of the set of parameters used in the classification. We also observed that the fifth most frequent nucleotide sequences obtained within the second approach are of different nature in case of human coronaviruses (different nucleotides for MERS, SARS-CoV, and SARS-CoV-2 versus identical nucleotides for four other coronaviruses). We expect that our findings will be useful as a supplementary tool in the classification of diseases caused by RNA viruses with respect to severity and contagiousness.

Ancient gene duplications in RNA viruses revealed by protein tertiary structure comparisons

To date only a handful of duplicated genes have been described in RNA viruses. This shortage can be attributed to different factors, including the RNA viruses with high mutation rate that would make a large genome more prone to acquire deleterious mutations. This may explain why sequence-based approaches have only found duplications in their most recent evolutionary history. To detect earlier duplications, we performed protein tertiary structure comparisons for every RNA virus family represented in the Protein Data Bank. We present a list of thirty pairs of possible paralogs with <30 per cent sequence identity. It is argued that these pairs are the outcome of six duplication events. These include the α and β subunits of the fungal toxin KP6 present in the dsRNA Ustilago maydis virus (family Totiviridae), the SARS-CoV (Coronaviridae) nsp3 domains SUD-N, SUD-M and X-domain, the Picornavirales (families Picornaviridae, Dicistroviridae, Iflaviridae and Secoviridae) capsid proteins VP1, VP2 and VP3, and the Enterovirus (family Picornaviridae) 3C and 2A cysteine-proteases. Protein tertiary structure comparisons may reveal more duplication events as more three-dimensional protein structures are determined and suggests that, although still rare, gene duplications may be more frequent in RNA viruses than previously thought. Keywords: gene duplications; RNA viruses.

Making sense of virus size and the tradeoffs shaping viral fitness

Viruses span an impressive size range, with genome length varying a thousandfold and virion volume nearly a millionfold. For cellular organisms the scaling of traits with size is a pervasive influence on ecological processes, but whether size plays a central role in viral ecology is unknown. Here, we focus on viruses of aquatic unicellular organisms, which exhibit the greatest known range of virus size. We outline hypotheses within a quantitative framework, and analyse data where available, to consider how size affects the primary components of viral fitness. We argue that larger viruses have fewer offspring per infection and slower contact rates with host cells, but a larger genome tends to increase infection efficiency, broaden host range, and potentially increase attachment success and decrease decay rate. These countervailing selective pressures may explain why a breadth of sizes exist and even coexist when infecting the same host populations. Oligotrophic ecosystems may be enriched in "giant" viruses, because environments with resource-limited phagotrophs at low concentrations may select for broader host range, better control of host metabolism, lower decay rate and a physical size that mimics bacterial prey. Finally, we describe where further research is needed to understand the ecology and evolution of viral size diversity.

Femtoplankton: What's New?

Since the discovery of high abundances of virus-like particles in aquatic environment, emergence of new analytical methods in microscopy and molecular biology has allowed significant advances in the characterization of the femtoplankton, i.e., floating entities filterable on a 0.2 µm pore size filter. The successive evidences in the last decade (2010-2020) of high abundances of biomimetic mineral-organic particles, extracellular vesicles, CPR/DPANN (Candidate phyla radiation/Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota and Nanohaloarchaeota), and very recently of aster-like nanoparticles (ALNs), show that aquatic ecosystems form a huge reservoir of unidentified and overlooked femtoplankton entities. The purpose of this review is to highlight this unsuspected diversity. Herein, we focus on the origin, composition and the ecological potentials of organic femtoplankton entities. Particular emphasis is given to the most recently discovered ALNs. All the entities described are displayed in an evolutionary context along a continuum of complexity, from minerals to cell-like living entities.

Evaluating assembly and variant calling software for strain-resolved analysis of large DNA viruses

Infection with human cytomegalovirus (HCMV) can cause severe complications in immunocompromised individuals and congenitally infected children. Characterizing heterogeneous viral populations and their evolution by high-throughput sequencing of clinical specimens requires the accurate assembly of individual strains or sequence variants and suitable variant calling methods. However, the performance of most methods has not been assessed for populations composed of low divergent viral strains with large genomes, such as HCMV. In an extensive benchmarking study, we evaluated 15 assemblers and 6 variant callers on 10 lab-generated benchmark data sets created with two different library preparation protocols, to identify best practices and challenges for analyzing such data. Most assemblers, especially metaSPAdes and IVA, performed well across a range of metrics in recovering abundant strains. However, only one, Savage, recovered low abundant strains and in a highly fragmented manner. Two variant callers, LoFreq and VarScan2, excelled across all strain abundances. Both shared a large fraction of false positive variant calls, which were strongly enriched in T to G changes in a 'G.G' context. The magnitude of this context-dependent systematic error is linked to the experimental protocol. We provide all benchmarking data, results and the entire benchmarking workflow named QuasiModo, Quasispecies Metric determination on omics, under the GNU General Public License v3.0 (https://github.com/hzi-bifo/Quasimodo), to enable full reproducibility and further benchmarking on these and other data.

Epidemiology and evolutionary analysis of Torque teno sus virus

Single stranded (ss) DNA viruses are increasingly being discovered due to the ongoing development of modern technologies in exploring the virosphere. Characterized by high rates of recombination and nucleotide substitutions, it could be comparable to RNA virus ones. Torque teno sus virus (TTSuV) is a standard ssDNA virus with a high population diversity, whose evolution is still obscure, further, it is frequently found in co-infections with other viruses threatening the porcine industry and therefore share the same host and epidemiological context. Here, we implement and describe approach to integrate viral nucleotide sequence analysis, surveillance data, and a structural approach to examine the evolution of TTSuVs, we collected samples from pigs displaying respiratory signs in China and revealed a high prevalence of TTSuV1 and TTSuVk2, frequently as part of co-infections with porcine circoviruses (PCVs), especially in spleen and lung. In addition, thirty six strains sequenced were obtained to investigate their genetic diversity in China. The evolutionary history of TTSuVs were unveiled as following: At the nucleotide sequence level, TTSuVs ORF1 was confirmed to be a robust phylogenetic maker to study evolution comparably to full genomes. Additionally, extensive recombination discovered within TTSuVk2a (also 5 out of the 36 sequenced strains in this study revealed to be recombination). Then, pairwise distance, phylogenetic trees, and amino acid analysis confirmed TTSuVs species, and allowed to define circulating genotypes (TTSuV1a-1, 1a-2, 1b-1, 1b-2, 1b-3, and k2a-1, k2a-2, k2b). Selection analysis uncovered seven and six positive selected sites in TTSuV1 and TTSuVk2, respectively. At the protein structure level, mapping of sites onto the three-dimensional structure revealed that several positive selected sites locate into potential epitopes, which might related to the potential escaping from host immune response. Our result could assist future studies on swine ssDNA virus classification, surveillance and control.

Viruses in the Built Environment (VIBE) meeting report

BACKGROUND

During a period of rapid growth in our understanding of the microbiology of the built environment in recent years, the majority of research has focused on bacteria and fungi. Viruses, while probably as numerous, have received less attention. In response, the Alfred P. Sloan Foundation supported a workshop entitled "Viruses in the Built Environment (VIBE)," at which experts in environmental engineering, environmental microbiology, epidemiology, infection prevention, fluid dynamics, occupational health, metagenomics, and virology convened to synthesize recent advances and identify key research questions and knowledge gaps regarding viruses in the built environment.

RESULTS

Four primary research areas and funding priorities were identified. First, a better understanding of viral communities in the built environment is needed, specifically which viruses are present and their sources, spatial and temporal dynamics, and interactions with bacteria. Second, more information is needed about viruses and health, including viral transmission in the built environment, the relationship between virus detection and exposure, and the definition of a healthy virome. The third research priority is to identify and evaluate interventions for controlling viruses and the virome in the built environment. This encompasses interactions among viruses, buildings, and occupants. Finally, to overcome the challenge of working with viruses, workshop participants emphasized that improved sampling methods, laboratory techniques, and bioinformatics approaches are needed to advance understanding of viruses in the built environment.

CONCLUSIONS

We hope that identifying these key questions and knowledge gaps will engage other investigators and funding agencies to spur future research on the highly interdisciplinary topic of viruses in the built environment. There are numerous opportunities to advance knowledge, as many topics remain underexplored compared to our understanding of bacteria and fungi. Video abstract.

Phylogenetic aggregation increases zoonotic potential of mammalian viruses

While many viruses of wild mammals are capable of infecting humans, our understanding of zoonotic potential is incomplete. Viruses vary in their degree of generalism, characterized by the phylogenetic relationships of their hosts. Among the dimensions of this phylogenetic landscape, phylogenetic aggregation, which is largely overlooked in studies of parasite host range, emerges in this study as a key predictor of zoonotic status of viruses. Plausibly, viruses that exhibit aggregation, typified by discrete clusters of related host species, may (i) have been able to close the phylogenetic distance to humans, (ii) have subsequently acquired an epidemiologically relevant host and (iii) exhibit relatively high fitness in realized host communities, which are frequently phylogenetically aggregated. These mechanisms associated with phylogenetic aggregation may help explain why correlated fundamental traits, such as the ability of viruses to replicate in the cytoplasm, are associated with zoonoses.

Adeno-Associated Virus Technologies and Methods for Targeted Neuronal Manipulation

Cell-type-specific expression of molecular tools and sensors is critical to construct circuit diagrams and to investigate the activity and function of neurons within the nervous system. Strategies for targeted manipulation include combinations of classical genetic tools such as Cre/loxP and Flp/FRT, use of cis-regulatory elements, targeted knock-in transgenic mice, and gene delivery by AAV and other viral vectors. The combination of these complex technologies with the goal of precise neuronal targeting is a challenge in the lab. This report will discuss the theoretical and practical aspects of combining current technologies and establish best practices for achieving targeted manipulation of specific cell types. Novel applications and tools, as well as areas for development, will be envisioned and discussed.

Viroids-First—A Model for Life on Earth, Mars and Exoplanets

The search for extraterrestrial life, recently fueled by the discovery of exoplanets, requires defined biosignatures. Current biomarkers include those of extremophilic organisms, typically archaea. Yet these cellular organisms are highly complex, which makes it unlikely that similar life forms evolved on other planets. Earlier forms of life on Earth may serve as better models for extraterrestrial life. On modern Earth, the simplest and most abundant biological entities are viroids and viruses that exert many properties of life, such as the abilities to replicate and undergo Darwinian evolution. Viroids have virus-like features, and are related to ribozymes, consisting solely of non-coding RNA, and may serve as more universal models for early life than do cellular life forms. Among the various proposed concepts, such as “proteins-first” or “metabolism-first”, we think that “viruses-first” can be specified to “viroids-first” as the most likely scenario for the emergence of life on Earth, and possibly elsewhere. With this article we intend to inspire the integration of virus research and the biosignatures of viroids and viruses into the search for extraterrestrial life.

Gene Birth Contributes to Structural Disorder Encoded by Overlapping Genes

The same nucleotide sequence can encode two protein products in different reading frames. Overlapping gene regions encode higher levels of intrinsic structural disorder (ISD) than nonoverlapping genes (39% vs. 25% in our viral dataset). This might be because of the intrinsic properties of the genetic code, because one member per pair was recently born de novo in a process that favors high ISD, or because high ISD relieves increased evolutionary constraint imposed by dual-coding. Here, we quantify the relative contributions of these three alternative hypotheses. We estimate that the recency of de novo gene birth explains [Formula: see text] or more of the elevation in ISD in overlapping regions of viral genes. While the two reading frames within a same-strand overlapping gene pair have markedly different ISD tendencies that must be controlled for, their effects cancel out to make no net contribution to ISD. The remaining elevation of ISD in the older members of overlapping gene pairs, presumed due to the need to alleviate evolutionary constraint, was already present prior to the origin of the overlap. Same-strand overlapping gene birth events can occur in two different frames, favoring high ISD either in the ancestral gene or in the novel gene; surprisingly, most de novo gene birth events contained completely within the body of an ancestral gene favor high ISD in the ancestral gene (23 phylogenetically independent events vs. 1). This can be explained by mutation bias favoring the frame with more start codons and fewer stop codons.

A Reference Viral Database (RVDB) To Enhance Bioinformatics Analysis of High-Throughput Sequencing for Novel Virus Detection

To facilitate bioinformatics analysis of high-throughput sequencing (HTS) data for the detection of both known and novel viruses, we have developed a new reference viral database (RVDB) that provides a broad representation of different virus species from eukaryotes by including all viral, virus-like, and virus-related sequences (excluding bacteriophages), regardless of their size. In particular, RVDB contains endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Sequences were clustered to reduce redundancy while retaining high viral sequence diversity. A particularly useful feature of RVDB is the reduction of cellular sequences, which can enhance the run efficiency of large transcriptomic and genomic data analysis and increase the specificity of virus detection.

Detection of distantly related viruses by high-throughput sequencing (HTS) is bioinformatically challenging because of the lack of a public database containing all viral sequences, without abundant nonviral sequences, which can extend runtime and obscure viral hits. Our reference viral database (RVDB) includes all viral, virus-related, and virus-like nucleotide sequences (excluding bacterial viruses), regardless of length, and with overall reduced cellular sequences. Semantic selection criteria (SEM-I) were used to select viral sequences from GenBank, resulting in a first-generation viral database (VDB). This database was manually and computationally reviewed, resulting in refined, semantic selection criteria (SEM-R), which were applied to a new download of updated GenBank sequences to create a second-generation VDB. Viral entries in the latter were clustered at 98% by CD-HIT-EST to reduce redundancy while retaining high viral sequence diversity. The viral identity of the clustered representative sequences (creps) was confirmed by BLAST searches in NCBI databases and HMMER searches in PFAM and DFAM databases. The resulting RVDB contained a broad representation of viral families, sequence diversity, and a reduced cellular content; it includes full-length and partial sequences and endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Testing of RVDBv10.2, with an in-house HTS transcriptomic data set indicated a significantly faster run for virus detection than interrogating the entirety of the NCBI nonredundant nucleotide database, which contains all viral sequences but also nonviral sequences. RVDB is publically available for facilitating HTS analysis, particularly for novel virus detection. It is meant to be updated on a regular basis to include new viral sequences added to GenBank.

IMPORTANCE To facilitate bioinformatics analysis of high-throughput sequencing (HTS) data for the detection of both known and novel viruses, we have developed a new reference viral database (RVDB) that provides a broad representation of different virus species from eukaryotes by including all viral, virus-like, and virus-related sequences (excluding bacteriophages), regardless of their size. In particular, RVDB contains endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Sequences were clustered to reduce redundancy while retaining high viral sequence diversity. A particularly useful feature of RVDB is the reduction of cellular sequences, which can enhance the run efficiency of large transcriptomic and genomic data analysis and increase the specificity of virus detection.

The Response of Heterotrophic Prokaryote and Viral Communities to Labile Organic Carbon Inputs Is Controlled by the Predator Food Chain Structure

Factors controlling the community composition of marine heterotrophic prokaryotes include organic-C, mineral nutrients, predation, and viral lysis. Two mesocosm experiments, performed at an Arctic location and bottom-up manipulated with organic-C, had very different results in community composition for both prokaryotes and viruses. Previously, we showed how a simple mathematical model could reproduce food web level dynamics observed in these mesocosms, demonstrating strong top-down control through the predator chain from copepods via ciliates and heterotrophic nanoflagellates. Here, we use a steady-state analysis to connect ciliate biomass to bacterial carbon demand. This gives a coupling of top-down and bottom-up factors whereby low initial densities of ciliates are associated with mineral nutrient-limited heterotrophic prokaryotes that do not respond to external supply of labile organic-C. In contrast, high initial densities of ciliates give carbon-limited growth and high responsiveness to organic-C. The differences observed in ciliate abundance, and in prokaryote abundance and community composition in the two experiments were in accordance with these predictions. Responsiveness in the viral community followed a pattern similar to that of prokaryotes. Our study provides a unique link between the structure of the predator chain in the microbial food web and viral abundance and diversity.

Predicting the Stability of Homologous Gene Duplications in a Plant RNA Virus

One of the striking features of many eukaryotes is the apparent amount of redundancy in coding and non-coding elements of their genomes. Despite the possible evolutionary advantages, there are fewer examples of redundant sequences in viral genomes, particularly those with RNA genomes. The factors constraining the maintenance of redundant sequences in present-day RNA virus genomes are not well known. Here, we use Tobacco etch virus, a plant RNA virus, to investigate the stability of genetically redundant sequences by generating viruses with potentially beneficial gene duplications. Subsequently, we tested the viability of these viruses and performed experimental evolution. We found that all gene duplication events resulted in a loss of viability or in a significant reduction in viral fitness. Moreover, upon analyzing the genomes of the evolved viruses, we always observed the deletion of the duplicated gene copy and maintenance of the ancestral copy. Interestingly, there were clear differences in the deletion dynamics of the duplicated gene associated with the passage duration and the size and position of the duplicated copy. Based on the experimental data, we developed a mathematical model to characterize the stability of genetically redundant sequences, and showed that fitness effects are not enough to predict genomic stability. A context-dependent recombination rate is also required, with the context being the duplicated gene and its position. Our results therefore demonstrate experimentally the deleterious nature of gene duplications in RNA viruses. Beside previously described constraints on genome size, we identified additional factors that reduce the likelihood of the maintenance of duplicated genes.